






"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   :							str          =		{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Any		=					"""deformable_detr"""
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					{
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }






    def __init__(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=True			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				Dict=3			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=300			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=1024			,		__SCREAMING_SNAKE_CASE	:				str=6			,		__SCREAMING_SNAKE_CASE	:				Any=1024			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=8			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=6			,		__SCREAMING_SNAKE_CASE	:				Any=1024			,		__SCREAMING_SNAKE_CASE	:				Tuple=8			,		__SCREAMING_SNAKE_CASE	:				str=0.0			,		__SCREAMING_SNAKE_CASE	:				Dict=True			,		__SCREAMING_SNAKE_CASE	:				List[str]="relu"			,		__SCREAMING_SNAKE_CASE	:				Tuple=256			,		__SCREAMING_SNAKE_CASE	:				List[str]=0.1			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.0			,		__SCREAMING_SNAKE_CASE	:				Tuple=0.0			,		__SCREAMING_SNAKE_CASE	:				List[str]=0.02			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=1.0			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=False			,		__SCREAMING_SNAKE_CASE	:				Tuple="sine"			,		__SCREAMING_SNAKE_CASE	:				Dict="resnet50"			,		__SCREAMING_SNAKE_CASE	:				str=True			,		__SCREAMING_SNAKE_CASE	:				int=False			,		__SCREAMING_SNAKE_CASE	:				str=4			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=4			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=4			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=False			,		__SCREAMING_SNAKE_CASE	:				int=300			,		__SCREAMING_SNAKE_CASE	:				int=False			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=1			,		__SCREAMING_SNAKE_CASE	:				Tuple=5			,		__SCREAMING_SNAKE_CASE	:				Dict=2			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=1			,		__SCREAMING_SNAKE_CASE	:				List[Any]=1			,		__SCREAMING_SNAKE_CASE	:				List[str]=5			,		__SCREAMING_SNAKE_CASE	:				List[Any]=2			,		__SCREAMING_SNAKE_CASE	:				str=0.1			,		__SCREAMING_SNAKE_CASE	:				Any=0.25			,		__SCREAMING_SNAKE_CASE	:				Any=False			,		**__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		)	->						Dict:
      if backbone_config is not None and use_timm_backbone:
        raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'   )

      if not use_timm_backbone:
        if backbone_config is None:
          logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
          lowerCamelCase_       	=		CONFIG_MAPPING['resnet'](out_features=['stage4']   )
        elif isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          lowerCamelCase_       	=		backbone_config.get('model_type'   )
          lowerCamelCase_       	=		CONFIG_MAPPING[backbone_model_type]
          lowerCamelCase_       	=		config_class.from_dict(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		use_timm_backbone
      lowerCamelCase_       	=		backbone_config
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		num_queries
      lowerCamelCase_       	=		max_position_embeddings
      lowerCamelCase_       	=		d_model
      lowerCamelCase_       	=		encoder_ffn_dim
      lowerCamelCase_       	=		encoder_layers
      lowerCamelCase_       	=		encoder_attention_heads
      lowerCamelCase_       	=		decoder_ffn_dim
      lowerCamelCase_       	=		decoder_layers
      lowerCamelCase_       	=		decoder_attention_heads
      lowerCamelCase_       	=		dropout
      lowerCamelCase_       	=		attention_dropout
      lowerCamelCase_       	=		activation_dropout
      lowerCamelCase_       	=		activation_function
      lowerCamelCase_       	=		init_std
      lowerCamelCase_       	=		init_xavier_std
      lowerCamelCase_       	=		encoder_layerdrop
      lowerCamelCase_       	=		auxiliary_loss
      lowerCamelCase_       	=		position_embedding_type
      lowerCamelCase_       	=		backbone
      lowerCamelCase_       	=		use_pretrained_backbone
      lowerCamelCase_       	=		dilation
      # deformable attributes
      lowerCamelCase_       	=		num_feature_levels
      lowerCamelCase_       	=		encoder_n_points
      lowerCamelCase_       	=		decoder_n_points
      lowerCamelCase_       	=		two_stage
      lowerCamelCase_       	=		two_stage_num_proposals
      lowerCamelCase_       	=		with_box_refine
      if two_stage is True and with_box_refine is False:
        raise ValueError('If two_stage is True, with_box_refine must be True.'   )
      # Hungarian matcher
      lowerCamelCase_       	=		class_cost
      lowerCamelCase_       	=		bbox_cost
      lowerCamelCase_       	=		giou_cost
      # Loss coefficients
      lowerCamelCase_       	=		mask_loss_coefficient
      lowerCamelCase_       	=		dice_loss_coefficient
      lowerCamelCase_       	=		bbox_loss_coefficient
      lowerCamelCase_       	=		giou_loss_coefficient
      lowerCamelCase_       	=		eos_coefficient
      lowerCamelCase_       	=		focal_alpha
      lowerCamelCase_       	=		disable_custom_kernels
      super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						int:
      return self.encoder_attention_heads






    @property
    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      return self.d_model






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Tuple:
      lowerCamelCase_       	=		copy.deepcopy(self.__dict__   )
      if self.backbone_config is not None:
        lowerCamelCase_       	=		self.backbone_config.to_dict()
      lowerCamelCase_       	=		self.__class__.model_type
      return output












"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




